Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA.
Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA; University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA.
Neuroimage. 2023 Apr 15;270:119946. doi: 10.1016/j.neuroimage.2023.119946. Epub 2023 Feb 17.
Characterizing the optimal fMRI paradigms for detecting behaviorally relevant functional connectivity (FC) patterns is a critical step to furthering our knowledge of the neural basis of behavior. Previous studies suggested that FC patterns derived from task fMRI paradigms, which we refer to as task-based FC, are better correlated with individual differences in behavior than resting-state FC, but the consistency and generalizability of this advantage across task conditions was not fully explored. Using data from resting-state fMRI and three fMRI tasks from the Adolescent Brain Cognitive Development Study ® (ABCD), we tested whether the observed improvement in behavioral prediction power of task-based FC can be attributed to changes in brain activity induced by the task design. We decomposed the task fMRI time course of each task into the task model fit (the fitted time course of the task condition regressors from the single-subject general linear model) and the task model residuals, calculated their respective FC, and compared the behavioral prediction performance of these FC estimates to resting-state FC and the original task-based FC. The FC of the task model fit was better than the FC of the task model residual and resting-state FC at predicting a measure of general cognitive ability or two measures of performance on the fMRI tasks. The superior behavioral prediction performance of the FC of the task model fit was content-specific insofar as it was only observed for fMRI tasks that probed similar cognitive constructs to the predicted behavior of interest. To our surprise, the task model parameters, the beta estimates of the task condition regressors, were equally if not more predictive of behavioral differences than all FC measures. These results showed that the observed improvement of behavioral prediction afforded by task-based FC was largely driven by the FC patterns associated with the task design. Together with previous studies, our findings highlighted the importance of task design in eliciting behaviorally meaningful brain activation and FC patterns.
对功能磁共振成像 (fMRI) 中的最佳范式进行特征分析，以检测与行为相关的功能连接 (FC) 模式，是深入了解行为神经基础的关键步骤。先前的研究表明，源自任务 fMRI 范式的 FC 模式（我们称之为任务型 FC）与行为个体差异的相关性优于静息态 FC，但这种优势在任务条件下的一致性和普遍性尚未得到充分探索。我们使用静息态 fMRI 数据和“青少年大脑认知发展研究”（ABCD）中的三项 fMRI 任务的数据，测试了任务型 FC 中观察到的行为预测能力的提高是否归因于任务设计引起的大脑活动变化。我们将每个任务的任务 fMRI 时间序列分解为任务模型拟合（从单个体线性模型中拟合任务条件回归器的时间序列）和任务模型残差，计算它们各自的 FC，并比较这些 FC 估计与静息态 FC 和原始任务型 FC 的行为预测性能。任务模型拟合的 FC 在预测一般认知能力的指标或 fMRI 任务的两个表现指标方面优于任务模型残差和静息态 FC。任务模型拟合的 FC 的优越行为预测性能是内容特定的，因为仅在探查与感兴趣的预测行为相似认知结构的 fMRI 任务中观察到。令我们惊讶的是，任务模型参数，即任务条件回归器的β估计，与所有 FC 指标一样，甚至更能预测行为差异。这些结果表明，任务型 FC 提供的行为预测的改善主要是由与任务设计相关的 FC 模式驱动的。与先前的研究一起，我们的研究结果强调了任务设计在引发有意义的行为激活和 FC 模式方面的重要性。
